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Critical Care Medicine

T3 - Tracking, trajectory and trigger tool

The Critical Care Unit is a dynamic, resource intense and data rich environment in which large streams of continuous physiologic data are available.  Rapid changes in physiologic variables drive equally rapid changes in decisions and treatment.  A vital aspect of critical care management is being able to perceive an evolving clinical picture, to be predictive rather than reactive and prescriptive, in our management practices.  This requires the visibility, interpretation and analysis of all available clinical, physiologic and laboratory data streams.



Developed by Dr Peter Laussen, Chief of the Department of Critical Care Medicine, and a colleague at Boston Children’s Hospital, Dr Mel Almodovar, T3 is a scalable, web-based program that allows the acquisition of continuous data at the bedside, to store it and present the data in a novel, integrated and interactive way to clinicians at the bedside, and allow for analysis and sharing of data for predictive modeling.  

In essence, it bridges the deficiencies of current physiologic monitoring devices at the bed side (which display in a pre-configured and vendor specific format, and don’t allow interaction with clinicians nor storage of data for analysis), and patient data captured in the electronic health record (which is periodic and also does not allow for integration or analysis).  T3 allows us to interact and use the data to see trends and evolving clinical problems in a clearer fashion, and to use the data to inform and support communication and management plans.

Continuous physiologic data has unique qualities of high velocity, large volume and wide variability. The captured data needs to be verified to enable accurate analysis and linked to other data sources to allow broad modelling.  We are fortunate to have the expertise of a senior data scientist, Andrew Goodwin, who has established an architecture for structuring data to allow rapid access for immediate solutions.  Our team is developing new real-time trajectory indices to help us predict an underlying and evolving physiologic state.